Assignment #5

Load the packages to be used and load the singer data set.

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(forcats))
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(gapminder))

Part 1: Factor management

Factors are how categorical data are stored. The values a factor can take on are called levels. It is important to check variable types. What you think are characters may actually be stored numerically. Let’s explore the data frame.

summary(gapminder)
        country        continent        year         lifeExp     
 Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
 Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
 Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
 Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
 Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
 Australia  :  12                  Max.   :2007   Max.   :82.60  
 (Other)    :1632                                                
      pop              gdpPercap       
 Min.   :6.001e+04   Min.   :   241.2  
 1st Qu.:2.794e+06   1st Qu.:  1202.1  
 Median :7.024e+06   Median :  3531.8  
 Mean   :2.960e+07   Mean   :  7215.3  
 3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
 Max.   :1.319e+09   Max.   :113523.1  
                                       

Before beginning the factor management exercise, let’s get to know the factors in the gapminder data set.

class(gapminder$country)
[1] "factor"
factor
class(gapminder$continent)
[1] "factor"
factor
class(gapminder$year)
[1] "integer"
integer
class(gapminder$lifeExp)
[1] "numeric"
numeric
class(gapminder$pop)
[1] "integer"
integer
class(gapminder$gdpPercap)
[1] "numeric"
numeric

We see that we have two factors in this data set. Let’s see the levels for “country” and “continent” before we begin filtering the data.

nlevels(gapminder$country)
[1] 142
nlevels(gapminder$continent)
[1] 5
levels(gapminder$country)
  [1] "Afghanistan"              "Albania"                 
  [3] "Algeria"                  "Angola"                  
  [5] "Argentina"                "Australia"               
  [7] "Austria"                  "Bahrain"                 
  [9] "Bangladesh"               "Belgium"                 
 [11] "Benin"                    "Bolivia"                 
 [13] "Bosnia and Herzegovina"   "Botswana"                
 [15] "Brazil"                   "Bulgaria"                
 [17] "Burkina Faso"             "Burundi"                 
 [19] "Cambodia"                 "Cameroon"                
 [21] "Canada"                   "Central African Republic"
 [23] "Chad"                     "Chile"                   
 [25] "China"                    "Colombia"                
 [27] "Comoros"                  "Congo, Dem. Rep."        
 [29] "Congo, Rep."              "Costa Rica"              
 [31] "Cote d'Ivoire"            "Croatia"                 
 [33] "Cuba"                     "Czech Republic"          
 [35] "Denmark"                  "Djibouti"                
 [37] "Dominican Republic"       "Ecuador"                 
 [39] "Egypt"                    "El Salvador"             
 [41] "Equatorial Guinea"        "Eritrea"                 
 [43] "Ethiopia"                 "Finland"                 
 [45] "France"                   "Gabon"                   
 [47] "Gambia"                   "Germany"                 
 [49] "Ghana"                    "Greece"                  
 [51] "Guatemala"                "Guinea"                  
 [53] "Guinea-Bissau"            "Haiti"                   
 [55] "Honduras"                 "Hong Kong, China"        
 [57] "Hungary"                  "Iceland"                 
 [59] "India"                    "Indonesia"               
 [61] "Iran"                     "Iraq"                    
 [63] "Ireland"                  "Israel"                  
 [65] "Italy"                    "Jamaica"                 
 [67] "Japan"                    "Jordan"                  
 [69] "Kenya"                    "Korea, Dem. Rep."        
 [71] "Korea, Rep."              "Kuwait"                  
 [73] "Lebanon"                  "Lesotho"                 
 [75] "Liberia"                  "Libya"                   
 [77] "Madagascar"               "Malawi"                  
 [79] "Malaysia"                 "Mali"                    
 [81] "Mauritania"               "Mauritius"               
 [83] "Mexico"                   "Mongolia"                
 [85] "Montenegro"               "Morocco"                 
 [87] "Mozambique"               "Myanmar"                 
 [89] "Namibia"                  "Nepal"                   
 [91] "Netherlands"              "New Zealand"             
 [93] "Nicaragua"                "Niger"                   
 [95] "Nigeria"                  "Norway"                  
 [97] "Oman"                     "Pakistan"                
 [99] "Panama"                   "Paraguay"                
[101] "Peru"                     "Philippines"             
[103] "Poland"                   "Portugal"                
[105] "Puerto Rico"              "Reunion"                 
[107] "Romania"                  "Rwanda"                  
[109] "Sao Tome and Principe"    "Saudi Arabia"            
[111] "Senegal"                  "Serbia"                  
[113] "Sierra Leone"             "Singapore"               
[115] "Slovak Republic"          "Slovenia"                
[117] "Somalia"                  "South Africa"            
[119] "Spain"                    "Sri Lanka"               
[121] "Sudan"                    "Swaziland"               
[123] "Sweden"                   "Switzerland"             
[125] "Syria"                    "Taiwan"                  
[127] "Tanzania"                 "Thailand"                
[129] "Togo"                     "Trinidad and Tobago"     
[131] "Tunisia"                  "Turkey"                  
[133] "Uganda"                   "United Kingdom"          
[135] "United States"            "Uruguay"                 
[137] "Venezuela"                "Vietnam"                 
[139] "West Bank and Gaza"       "Yemen, Rep."             
[141] "Zambia"                   "Zimbabwe"                
Afghanistan

Albania

Algeria

Angola

Argentina

Australia

Austria

Bahrain

Bangladesh

Belgium

Benin

Bolivia

Bosnia and Herzegovina

Botswana

Brazil

Bulgaria

Burkina Faso

Burundi

Cambodia

Cameroon

Canada

Central African Republic

Chad

Chile

China

Colombia

Comoros

Congo, Dem. Rep.

Congo, Rep.

Costa Rica

Cote d'Ivoire

Croatia

Cuba

Czech Republic

Denmark

Djibouti

Dominican Republic

Ecuador

Egypt

El Salvador

Equatorial Guinea

Eritrea

Ethiopia

Finland

France

Gabon

Gambia

Germany

Ghana

Greece

Guatemala

Guinea

Guinea-Bissau

Haiti

Honduras

Hong Kong, China

Hungary

Iceland

India

Indonesia

Iran

Iraq

Ireland

Israel

Italy

Jamaica

Japan

Jordan

Kenya

Korea, Dem. Rep.

Korea, Rep.

Kuwait

Lebanon

Lesotho

Liberia

Libya

Madagascar

Malawi

Malaysia

Mali

Mauritania

Mauritius

Mexico

Mongolia

Montenegro

Morocco

Mozambique

Myanmar

Namibia

Nepal

Netherlands

New Zealand

Nicaragua

Niger

Nigeria

Norway

Oman

Pakistan

Panama

Paraguay

Peru

Philippines

Poland

Portugal

Puerto Rico

Reunion

Romania

Rwanda

Sao Tome and Principe

Saudi Arabia

Senegal

Serbia

Sierra Leone

Singapore

Slovak Republic

Slovenia

Somalia

South Africa

Spain

Sri Lanka

Sudan

Swaziland

Sweden

Switzerland

Syria

Taiwan

Tanzania

Thailand

Togo

Trinidad and Tobago

Tunisia

Turkey

Uganda

United Kingdom

United States

Uruguay

Venezuela

Vietnam

West Bank and Gaza

Yemen, Rep.

Zambia

Zimbabwe
levels(gapminder$continent)
[1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania" 
Africa

Americas

Asia

Europe

Oceania

Now let’s work towards dropping “Oceoania”.

We will start by filtering the gapminder data to remove observations associated with the continent of Oceania.

No_Oceania <- gapminder %>%
  filter(continent == "Africa" | continent =="Americas"|continent == "Asia"|continent == "Europe")
No_Oceania %>% 
  sample_frac(0.1) %>% #just showing a sample of the data
  knitr:: kable(format = "markdown", justify = "centre")
country continent year lifeExp pop gdpPercap
Austria Europe 1962 69.540 7129864 10750.7211
Lebanon Asia 1977 66.099 3115787 8659.6968
Tunisia Africa 1997 71.973 9231669 4876.7986
Swaziland Africa 1962 44.992 370006 1856.1821
Afghanistan Asia 1992 41.674 16317921 649.3414
Haiti Americas 2002 58.137 7607651 1270.3649
Mexico Americas 1967 60.110 47995559 5754.7339
Myanmar Asia 1972 53.070 28466390 357.0000
Liberia Africa 1967 41.536 1279406 713.6036
Nepal Asia 2002 61.340 25873917 1057.2063
El Salvador Americas 2007 71.878 6939688 5728.3535
Canada Americas 1987 76.860 26549700 26626.5150
Congo, Rep. Africa 1972 54.907 1340458 3213.1527
France Europe 1952 67.410 42459667 7029.8093
Niger Africa 1952 37.444 3379468 761.8794
Turkey Europe 1962 52.098 29788695 2322.8699
Benin Africa 2002 54.406 7026113 1372.8779
Japan Asia 1967 71.430 100825279 9847.7886
Jamaica Americas 1982 71.210 2298309 6068.0513
Congo, Dem. Rep. Africa 1992 45.548 41672143 457.7192
Albania Europe 1982 70.420 2780097 3630.8807
Libya Africa 1992 68.755 4364501 9640.1385
Sweden Europe 1972 74.720 8122293 17832.0246
Mongolia Asia 1952 42.244 800663 786.5669
Equatorial Guinea Africa 1967 38.987 259864 915.5960
Serbia Europe 1957 61.685 7271135 4981.0909
Comoros Africa 1982 52.933 348643 1267.1001
Equatorial Guinea Africa 1987 45.664 341244 966.8968
Syria Asia 1957 48.284 4149908 2117.2349
Bosnia and Herzegovina Europe 1997 73.244 3607000 4766.3559
Djibouti Africa 1977 46.519 228694 3081.7610
Mexico Americas 1982 67.405 71640904 9611.1475
Romania Europe 2007 72.476 22276056 10808.4756
Namibia Africa 1962 48.386 621392 3173.2156
Syria Asia 1972 57.296 6701172 2571.4230
Singapore Asia 1972 69.521 2152400 8597.7562
Guinea Africa 1967 37.197 3451418 708.7595
Chile Americas 1957 56.074 7048426 4315.6227
Korea, Dem. Rep. Asia 1962 56.656 10917494 1621.6936
Guinea-Bissau Africa 1987 41.245 927524 736.4154
Belgium Europe 1987 75.350 9870200 22525.5631
Morocco Africa 1957 45.423 11406350 1642.0023
Denmark Europe 1962 72.350 4646899 13583.3135
Egypt Africa 1977 53.319 38783863 2785.4936
Equatorial Guinea Africa 1982 43.662 285483 927.8253
Yemen, Rep. Asia 1952 32.548 4963829 781.7176
Vietnam Asia 1962 45.363 33796140 772.0492
Burkina Faso Africa 1972 43.591 5433886 854.7360
Equatorial Guinea Africa 1972 40.516 277603 672.4123
Bolivia Americas 1997 62.050 7693188 3326.1432
Hungary Europe 1962 67.960 10063000 7550.3599
Tunisia Africa 1977 59.837 6005061 3120.8768
Vietnam Asia 1987 62.820 62826491 820.7994
Argentina Americas 1982 69.942 29341374 8997.8974
Bahrain Asia 1962 56.923 171863 12753.2751
Thailand Asia 1982 64.597 48827160 2393.2198
Uganda Africa 1982 49.849 12939400 682.2662
Canada Americas 1962 71.300 18985849 13462.4855
Belgium Europe 2002 78.320 10311970 30485.8838
Italy Europe 2007 80.546 58147733 28569.7197
Thailand Asia 1957 53.630 25041917 793.5774
Iraq Asia 2002 57.046 24001816 4390.7173
Cuba Americas 1977 72.649 9537988 6380.4950
Morocco Africa 2002 69.615 31167783 3258.4956
Greece Europe 1967 71.000 8716441 8513.0970
Rwanda Africa 1962 43.000 3051242 597.4731
Cote d’Ivoire Africa 1967 47.350 4744870 2052.0505
Korea, Dem. Rep. Asia 1977 67.159 16325320 4106.3012
Niger Africa 2007 56.867 12894865 619.6769
Jamaica Americas 2002 72.047 2664659 6994.7749
Kuwait Asia 1977 69.343 1140357 59265.4771
Pakistan Asia 1972 51.929 69325921 1049.9390
Italy Europe 1977 73.480 56059245 14255.9847
Norway Europe 1982 75.970 4114787 26298.6353
Nicaragua Americas 1972 55.151 2182908 4688.5933
Botswana Africa 1967 53.298 553541 1214.7093
Greece Europe 1957 67.860 8096218 4916.2999
Tanzania Africa 1987 51.535 23040630 831.8221
South Africa Africa 1952 45.009 14264935 4725.2955
Eritrea Africa 1987 46.453 2915959 521.1341
Trinidad and Tobago Americas 1962 64.900 887498 4997.5240
Iraq Asia 2007 59.545 27499638 4471.0619
Israel Asia 1997 78.269 5531387 20896.6092
Albania Europe 1962 64.820 1728137 2312.8890
Congo, Rep. Africa 1967 52.040 1179760 2677.9396
Algeria Africa 2007 72.301 33333216 6223.3675
Cameroon Africa 1962 42.643 5793633 1399.6074
Cote d’Ivoire Africa 2002 46.832 16252726 1648.8008
Myanmar Asia 1982 58.056 34680442 424.0000
Korea, Rep. Asia 1982 67.123 39326000 5622.9425
Denmark Europe 2002 77.180 5374693 32166.5001
Turkey Europe 1957 48.079 25670939 2218.7543
Libya Africa 1952 42.723 1019729 2387.5481
Thailand Asia 1997 67.521 60216677 5852.6255
Germany Europe 1977 72.500 78160773 20512.9212
Portugal Europe 1977 70.410 9662600 10172.4857
Puerto Rico Americas 2007 78.746 3942491 19328.7090
Brazil Americas 2002 71.006 179914212 8131.2128
Sierra Leone Africa 2007 42.568 6144562 862.5408
Mauritius Africa 1957 58.089 609816 2034.0380
Ghana Africa 1982 53.744 11400338 876.0326
Bulgaria Europe 1997 70.320 8066057 5970.3888
Ethiopia Africa 1982 44.916 38111756 577.8607
Somalia Africa 1967 38.977 3428839 1284.7332
Jordan Asia 1987 65.869 2820042 4448.6799
Peru Americas 1967 51.445 12132200 5788.0933
Rwanda Africa 1972 44.600 3992121 590.5807
Bulgaria Europe 1972 70.900 8576200 6597.4944
Egypt Africa 1962 46.992 28173309 1693.3359
Mongolia Asia 2007 66.803 2874127 3095.7723
Finland Europe 2007 79.313 5238460 33207.0844
Honduras Americas 1977 57.402 3055235 3203.2081
Sao Tome and Principe Africa 1982 60.351 98593 1890.2181
Cuba Americas 1972 70.723 8831348 5305.4453
Sweden Europe 1992 78.160 8718867 23880.0168
Czech Republic Europe 1997 74.010 10300707 16048.5142
South Africa Africa 1987 60.834 35933379 7825.8234
Czech Republic Europe 1972 70.290 9862158 13108.4536
Montenegro Europe 1987 74.865 569473 11732.5102
Denmark Europe 1987 74.800 5127024 25116.1758
Iceland Europe 1982 76.990 233997 23269.6075
Afghanistan Asia 1997 41.763 22227415 635.3414
Guatemala Americas 1972 53.738 5149581 4031.4083
Niger Africa 1992 47.391 8392818 581.1827
Central African Republic Africa 1987 50.485 2840009 844.8764
Paraguay Americas 1982 66.874 3366439 4258.5036
Trinidad and Tobago Americas 1987 69.582 1191336 7388.5978
Lebanon Asia 1972 65.421 2680018 7486.3843
Costa Rica Americas 1987 74.752 2799811 5629.9153
Cote d’Ivoire Africa 1972 49.801 6071696 2378.2011
Angola Africa 2007 42.731 12420476 4797.2313
Austria Europe 1992 76.040 7914969 27042.0187
Belgium Europe 1992 76.460 10045622 25575.5707
Congo, Dem. Rep. Africa 1962 42.122 17486434 896.3146
Guinea Africa 1952 33.609 2664249 510.1965
Trinidad and Tobago Americas 2002 68.976 1101832 11460.6002
Rwanda Africa 1992 23.599 7290203 737.0686
Afghanistan Asia 1987 40.822 13867957 852.3959
Senegal Africa 1997 60.187 9535314 1392.3683
Panama Americas 1992 72.462 2484997 6618.7431
Kenya Africa 1957 44.686 7454779 944.4383
Norway Europe 1957 73.440 3491938 11653.9730
Panama Americas 1972 66.216 1616384 5364.2497
Iran Asia 1992 65.742 60397973 7235.6532
Sudan Africa 1982 50.338 20367053 1895.5441
Lesotho Africa 2002 44.593 2046772 1275.1846
West Bank and Gaza Asia 2002 72.370 3389578 4515.4876
Slovenia Europe 1987 72.250 1945870 18678.5349
Angola Africa 1962 34.000 4826015 4269.2767
Mauritius Africa 1972 62.944 851334 2575.4842
Hong Kong, China Asia 1967 70.000 3722800 6197.9628
Hong Kong, China Asia 2002 81.495 6762476 30209.0152
Slovak Republic Europe 1977 70.450 4827803 10922.6640
Bangladesh Asia 1977 46.923 80428306 659.8772
Gabon Africa 1967 44.598 489004 8358.7620
Denmark Europe 1972 73.470 4991596 18866.2072
Korea, Rep. Asia 1987 69.810 41622000 8533.0888
Swaziland Africa 1967 46.633 420690 2613.1017
Cambodia Asia 1962 43.415 6083619 496.9136
Japan Asia 1987 78.670 122091325 22375.9419
Korea, Rep. Asia 1992 72.244 43805450 12104.2787
Chile Americas 1997 75.816 14599929 10118.0532
Spain Europe 1972 73.060 34513161 10638.7513
Central African Republic Africa 1957 37.464 1392284 1190.8443
Djibouti Africa 1962 39.693 89898 3020.9893
Uruguay Americas 1992 72.752 3149262 8137.0048
Swaziland Africa 1992 58.474 962344 3553.0224
Panama Americas 1977 68.681 1839782 5351.9121

Let’s check how many levels there are and whether we need to remove unused factor levels.

nlevels(No_Oceania$continent)
[1] 5
levels(No_Oceania$continent)
[1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania" 
Africa

Americas

Asia

Europe

Oceania

We see that there are still 5 levels. Let’s remove the unused factor levels.

Drop_Oceania <- No_Oceania %>% 
  droplevels()
nlevels(Drop_Oceania$continent)
[1] 4
levels(Drop_Oceania$continent)
[1] "Africa"   "Americas" "Asia"     "Europe"  
Africa

Americas

Asia

Europe

We do note that the factor levels are ordered alphabetically. Let’s use forcats to re-order the factor levels. We can re-order in different ways.

One way to re-order is by frequency.For example, the frequency of continents in the data.

Drop_Oceania$continent %>% 
  fct_infreq() %>% 
  levels()
[1] "Africa"   "Asia"     "Europe"   "Americas"
Africa

Asia

Europe

Americas

We can also re-order by the value of other variables in the data as life expectanc or gdp.

Drop_Oceania_1980 <- Drop_Oceania %>% 
  filter(year > 1979) %>% 
  group_by(continent, year) %>% 
  mutate(mediangdp = median(gdpPercap))

Drop_Oceania_1980
fct_reorder(Drop_Oceania_1980$continent, Drop_Oceania_1980$gdpPercap, min) %>% 
  levels() %>% head()
[1] "Africa"   "Asia"     "Americas" "Europe"  
Africa

Asia

Americas

Europe

Now we can put this into graph form.

ggplot(Drop_Oceania_1980, aes(x = year, y = Drop_Oceania_1980$mediangdp, color = fct_reorder2(continent, year, Drop_Oceania_1980$mediangdp))) +
  geom_line() +
  labs(color = "Continent")+
  xlab("Year") + ylab("Median GDP") +
  ggtitle("Median GDP 1980-2007 by Continent")

We see that reordering the levels allows the legend to be organized in the same fashion as the trendlines.

Part 2 File input and output

Let’s try saving some wrangled data into a new file.

trial<- gapminder %>%
  filter(year == "2007")

write.csv(trial, file = "assignment5_stat545")

Now let’s import this file to see if we can read it.

read.csv("assignment5_stat545")

Part 3 Visualization design

Here is an old graph from assignment 3. It looks at the weighted mean life expectancy for continents over time for the gapminder data set.

gapminder %>% 
 group_by(continent, year) %>%
 summarise(mean_lifeExp_weighted = weighted.mean(lifeExp, pop)) %>% 
 ggplot(aes(year, mean_lifeExp_weighted))+
  geom_point(aes(colour = continent))

What can be improved? 1) We can add a trend line so we can estimate what is happening between years. 2) We can change the axis titles and add a graph title. 3) We can re-order the factor levels so the legend is presented in logical order. 4) We can change the scale of the axes to show every 5 years of life. 5) We can change the graphs theme.

gapminder %>% 
 group_by(continent, year) %>%
 summarise(mean_lifeExp_weighted = weighted.mean(lifeExp, pop)) %>% 
 ggplot(aes(year, mean_lifeExp_weighted, color = fct_reorder2(continent, year, mean_lifeExp_weighted)))+
  geom_point(aes(colour = continent))+
  geom_line(aes(colour = continent))+
  scale_y_continuous(breaks=5*(1:17))+
  xlab("Year")+
    ylab("Weighted Mean Life Expectancy")+
  labs(color = "Continent")+
  ggtitle("Weighted Mean Life Expectancy vs. Time (Years)")+
  theme_minimal()

**Let’s try a different graph and also use plotly to make it more interactive.*Now we can make an interactive plot using plotly.**

Here is the original graph:

gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent)

Here is the cleaner version.

gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ #change the scale
  xlab("Year")+ #change the titles
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+ #make the legend clearer
  theme(legend.position="right") #select legend position

Now, let’s make this into a plotly graph.

#First, let's save the above graph into "plotly1"
plotly1 <- gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ 
  xlab("Year")+
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+
  theme(legend.position="right") 

#Now open plotly package.
library(plotly)

Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':

    last_plot
The following object is masked from 'package:stats':

    filter
The following object is masked from 'package:graphics':

    layout
#GEnerating plotly graph.
ggplotly(plotly1)

**We do notice that plotly has some nice features. However, visualization is not as good. The legend is cut off. We can see if moving the legend elsewhere will improve this visualization.

plotly2 <- gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ 
  xlab("Year")+
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+
  theme(legend.position="bottom") 

#GEnerating plotly graph.
ggplotly(plotly2)

Hmmmm. It appears plotly dose not have all the aesthetic options that ggplot does. Anyways, let’s save this as a local html file.

ggplotly(plotly1) %>% 
  htmlwidgets::saveWidget("plotly1")

Let’s save this plot using ggsave(). This will generate an image file where we can specify certain aspects such as dimension and resolution.

plotly1

ggsave("plotly1.png", width = 20, height = 20, units = "cm", dpi = 300)
---
title: "hw05-ntjjmak"
author: "Nicole Mak"
date: "15/10/2018"
output:
    github_document:
      html_notebook
---

# Assignment #5


**Load the packages to be used and load the singer data set.**

```{r}

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(forcats))
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(gapminder))
```

## Part 1: Factor management

**Factors are how categorical data are stored. The values a factor can take on are called levels. It is important to check variable types. What you think are characters may actually be stored numerically. Let's explore the data frame.**

```{r}
summary(gapminder)
```

**Before beginning the factor management exercise, let's get to know the factors in the gapminder data set.**

```{r}
class(gapminder$country)
class(gapminder$continent)
class(gapminder$year)
class(gapminder$lifeExp)
class(gapminder$pop)
class(gapminder$gdpPercap)

```

**We see that we have two factors in this data set. Let's see the levels for "country" and "continent" before we begin filtering the data.**

```{r}
nlevels(gapminder$country)
nlevels(gapminder$continent)

levels(gapminder$country)
levels(gapminder$continent)
```


**Now let's work towards dropping "Oceoania".**

**We will start by filtering the gapminder data to remove observations associated with the continent of Oceania.**

```{r}
No_Oceania <- gapminder %>%
  filter(continent == "Africa" | continent =="Americas"|continent == "Asia"|continent == "Europe")
No_Oceania %>% 
  sample_frac(0.1) %>% #just showing a sample of the data
  knitr:: kable(format = "markdown", justify = "centre")
```

**Let's check how many levels there are and whether we need to remove unused factor levels.**
```{r}
nlevels(No_Oceania$continent)
levels(No_Oceania$continent)
```

**We see that there are still 5 levels. Let's remove the unused factor levels.**

```{r}
Drop_Oceania <- No_Oceania %>% 
  droplevels()
nlevels(Drop_Oceania$continent)
levels(Drop_Oceania$continent)
```

**We do note that the factor levels are ordered alphabetically. Let's use forcats to re-order the factor levels. We can re-order in different ways.**

**One way to re-order is by frequency.For example, the frequency of continents in the data.**

```{r}
Drop_Oceania$continent %>% 
  fct_infreq() %>% 
  levels()

```

**We can also re-order by the value of other variables in the data as life expectanc or gdp.**

```{r}
Drop_Oceania_1980 <- Drop_Oceania %>% 
  filter(year > 1979) %>% 
  group_by(continent, year) %>% 
  mutate(mediangdp = median(gdpPercap))

Drop_Oceania_1980


fct_reorder(Drop_Oceania_1980$continent, Drop_Oceania_1980$gdpPercap, min) %>% 
  levels() %>% head()
```

**Now we can put this into graph form.**

```{r}
ggplot(Drop_Oceania_1980, aes(x = year, y = Drop_Oceania_1980$mediangdp, color = fct_reorder2(continent, year, Drop_Oceania_1980$mediangdp))) +
  geom_line() +
  labs(color = "Continent")+
  xlab("Year") + ylab("Median GDP") +
  ggtitle("Median GDP 1980-2007 by Continent")
```

**We see that reordering the levels allows the legend to be organized in the same fashion as the trendlines.**


## Part 2 File input and output

**Let's try saving some wrangled data into a new file.**

```{r}
trial<- gapminder %>%
  filter(year == "2007")

write.csv(trial, file = "assignment5_stat545")
```

**Now let's import this file to see if we can read it.**

```{r}
read.csv("assignment5_stat545")
```



## Part 3 Visualization design


**Here is an old graph from assignment 3. It looks at the weighted mean life expectancy for continents over time for the gapminder data set.**

```{r}
gapminder %>% 
 group_by(continent, year) %>%
 summarise(mean_lifeExp_weighted = weighted.mean(lifeExp, pop)) %>% 
 ggplot(aes(year, mean_lifeExp_weighted))+
  geom_point(aes(colour = continent))
```

**What can be improved? 1) We can add a trend line so we can estimate what is happening between years. 2) We can change the axis titles and add a graph title. 3) We can re-order the factor levels so the legend is presented in logical order. 4) We can change the scale of the axes to show every 5 years of life. 5) We can change the graphs theme.**

```{r}
gapminder %>% 
 group_by(continent, year) %>%
 summarise(mean_lifeExp_weighted = weighted.mean(lifeExp, pop)) %>% 
 ggplot(aes(year, mean_lifeExp_weighted, color = fct_reorder2(continent, year, mean_lifeExp_weighted)))+
  geom_point(aes(colour = continent))+
  geom_line(aes(colour = continent))+
  scale_y_continuous(breaks=5*(1:17))+
  xlab("Year")+
    ylab("Weighted Mean Life Expectancy")+
  labs(color = "Continent")+
  ggtitle("Weighted Mean Life Expectancy vs. Time (Years)")+
  theme_minimal()
  
```


**Let's try a different graph and also use `plotly` to make it more interactive.*Now we can make an interactive plot using `plotly`.**

**Here is the original graph:**

```{r}
gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent)
```

**Here is the cleaner version.**

```{r}
gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ #change the scale
  xlab("Year")+ #change the titles
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+ #make the legend clearer
  theme(legend.position="right") #select legend position
```


**Now, let's make this into a `plotly` graph.**

```{r}
#First, let's save the above graph into "plotly1"
plotly1 <- gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ 
  xlab("Year")+
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+
  theme(legend.position="right") 

#Now open plotly package.
library(plotly)

#GEnerating plotly graph.
ggplotly(plotly1)

```

**We do notice that `plotly` has some nice features. However, visualization is not as good. The legend is cut off. We can see if moving the legend elsewhere will improve this visualization.

```{r}
plotly2 <- gapminder %>%
 group_by(year) %>% 
 mutate(median = median(lifeExp)) %>%
 ggplot(aes(year, lifeExp)) +
 geom_jitter(aes(colour = (lifeExp < median)), alpha = 0.5)+
  facet_wrap(~ continent,nrow = 5, ncol = 1)+
  scale_y_continuous(breaks=10*(1:9))+ 
  xlab("Year")+
  ylab("Life Expectancy")+
  ggtitle("Country life expectancy over time")+
  scale_colour_discrete(name  ="CountrylLife expectancy > worldwide median?")+
  theme(legend.position="bottom") 

#GEnerating plotly graph.
ggplotly(plotly2)
```

**Hmmmm. It appears `plotly` dose not have all the aesthetic options that ggplot does. Anyways, let's save this as a local html file.**
```{r}
ggplotly(plotly1) %>% 
  htmlwidgets::saveWidget("plotly1")
```

**Let's save this plot using `ggsave()`. This will generate an image file where we can specify certain aspects such as dimension and resolution.**

```{r}
plotly1

ggsave("plotly1.png", width = 20, height = 20, units = "cm", dpi = 300)
```

